code: def __init__(self, num_classes=10):
      super(Alexnet_Top, self).__init__()
      self.layer3 = nn.Sequential(
          ConvBlock(384, 256, kernel_size=3, stride=1, padding=1),
          nn.BatchNorm2d(256),
          nn.ReLU(inplace=True),
      )
      self.layer4 = nn.Sequential(
          ConvBlock(256, 256, kernel_size=3, stride=1, padding=1),
          nn.BatchNorm2d(256),
          nn.ReLU(inplace=True),
          nn.MaxPool2d(kernel_size=2, stride=2),
      )
      self.classifier = nn.Sequential(
          nn.Linear(4096, num_classes),
      )
  
imports: from torch.nn import init
  import sys
  import torch
  import torch
  import torchvision
  from torch import nn
  from datetime import datetime
  from fate.ml.nn.homo.fedavg import FedAVGClient, TrainingArguments
  from fate.ml.nn.model_zoo.agg_layer.fedpass._passport_block import ConvPassportBlock
  from fate.ml.nn.model_zoo.agg_layer.fedpass.agg_layer import FedPassAggLayerHost, FedPassAggLayerGuest
  from tqdm import tqdm
  from fate.ml.nn.model_zoo.hetero_nn_model import HeteroNNModelGuest, HeteroNNModelHost
  from fate.ml.nn.hetero.hetero_nn import HeteroNNTrainerGuest, HeteroNNTrainerHost, TrainingArguments
label_desc: Use image node_id 1 to create a new Alexnet_Top object from inherited base classes: nn with example: obj = Alexnet_Top(num_classes)